Context Switching Algorithm for Selective Multibiometric Fusion

  • Mayank Vatsa
  • Richa Singh
  • Afzel Noore
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)


This paper presents a multimodal biometric fusion algorithm that supports biometric image quality and case-based context switching approach for selecting appropriate constituent unimodal traits and fusion algorithms. Depending on the quality of input samples, the proposed algorithm intelligently selects appropriate fusion algorithm for optimal performance. Experiments and correlation analysis on a multimodal database of 320 subjects show that the context switching algorithm improves the verification performance both in terms of accuracy and time.


Fusion Rule Fusion Algorithm Biometric System Context Switching Image Quality Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ross, A., Nandakumar, K., Jain, A.: Handbook of multibiometrics. Springer, Heidelberg (2006)Google Scholar
  2. 2.
    Vatsa, M., Singh, R., Noore, A.: SVM based adaptive biometric image enhancement using quality assessment. In: Speech, Audio, Image and Biomedical Signal Processing using Neural Networks, vol. 83, pp. 351–371. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Vatsa, M., Singh, R., Noore, A., Houck, M.: Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory. International Journal of Approximate Reasoning 50(1), 51–61 (2009)CrossRefGoogle Scholar
  4. 4.
    Kalka, N.D., Zuo, J., Dorairaj, V., Schmid, N.A., Cukic, B.: Image quality assessment for iris biometric. In: Proceedings of SPIE Conference on Biometric Technology for Human Identification III, vol. 6202, pp. 61020D-1–62020D-11 (2006)Google Scholar
  5. 5.
    Singh, R., Vatsa, M., Noore, A.: Face recognition with disguise and single gallery images. Image and Vision Computing 27(3), 245–257 (2009)CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Vatsa, M., Singh, R., Ross, A., Noore, A.: Likelihood ratio in a SVM framework: fusing linear and non-linear classifiers. In: Proceedings of IEEE Computer Society Workshop on Biometrics at Computer Vision and Pattern Recognition Conference, pp. 1–6 (2008)Google Scholar
  8. 8.
    Tao, Q., Wu, G., Wang, F., Wang, J.: Posterior probability support vector machines for unbalanced data. IEEE Transaction on Neural Network 16(6), 1561–1573 (2005)CrossRefGoogle Scholar
  9. 9.
    Kuncheva, L.I., Whitaker, C.J., Shipp, C.A., Duin, R.P.W.: Is independence good for combining classifiers? In: Proceedings of International Conference on Pattern Recognition, vol. 2, pp. 168–171 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mayank Vatsa
    • 1
  • Richa Singh
    • 1
  • Afzel Noore
    • 2
  1. 1.IIIT DelhiIndia
  2. 2.West Virginia UniversityUSA

Personalised recommendations